SCH: INT: Novel Techniques for Patient-centric Disease Management using Automatically Inferred Behavioral Biomarkers and Sensor-Supported Contextual Self-Report

以患者为中心的，个性化的，精准的医疗和健康的愿景将完全实现，只有当一个人？的自我保健和临床决策是由一个丰富的，预测模型的个人？的健康状况。移动的技术的发展和传播为以更细粒度、更不显眼甚至更实惠的方式收集高度详细和个性化的数据创造了前所未有的机会;这些数据包括活动水平、位置模式、睡眠、消费以及通信和社交互动。然而，将这种潜力转化为实践需要我们开发算法和方法，将这些原始数据转化为可操作的信息。该研究将开发新的和可推广的技术，以获得与个人健康和临床决策相关的稳健措施。 该团队将开发和评估将原始人类活动数据转换为临床可操作的行为生物标志物的工具。这就要求创造性地使用基础技术能力（即，被动数据采集、数据分析和机器学习、数据可视化、用户体验），以及对潜在健康状况和管理的严格理解（即功能性健康指标、可实现和最佳健康结果、患者依从性挑战、与药物和治疗其他方面相关的风险和益处以及临床决策）。该方法在疾病管理中具有广泛的适用性（例如，自身免疫、胃肠道、抑郁、认知下降和神经系统疾病），但也需要针对特定条件和个体进行定制。因此，我们将在特定的背景下进行这项初步工作，即针对三种突出疾病的慢性疼痛管理：类风湿性关节炎，骨关节炎和下背痛。与我们最初的目标领域疼痛管理相关的行为生物标志物集中在：（i）活动水平下降;（ii）压力增加;（iii）睡眠质量下降;（iv）功能下降，例如，旅行距离缩短或无法上班。先前已经证明了移动的电话的被动感测能力跟踪睡眠、活动水平的变化、压力、社会孤立、地理位置和可能是疼痛干扰的前驱或症状的若干其他指标的有效性。虽然行为生物标志物广泛依赖于被动捕获的数据流（如活动，位置，通信，应用程序使用和音频），但仍然存在需要自我报告数据来增强或澄清被动收集的数据的重要情况。然而，评估相关症状和行为的标准化患者调查工具不适合每天使用，因为长度，问题设计或两者兼而有之。此外，传统形式的自我报告往往是侵入性的，繁琐的，并遭受高流失率。一种新的方法，上下文回忆，旨在通过三个关键机制来减轻与自我报告相关的问题：优化提示的传递，为用户提供关键的上下文线索，以提高回忆，并采用视觉输入技术作为替代长形式的措施，不扩展以及频繁的移动的自我报告。个性化疾病管理的方法在可负担性和可获得性方面有意可扩展。被动数据收集不需要用户的注意，情境回忆是一种自我报告的形式，专为忙碌的个人设计，他们的时间有一系列的要求和限制，以及潜在的识字和算术限制。这种方法的面向临床医生的组件也被设计为在资源受限的临床环境中工作，其中临床医生处于特定的时间压力下。该团队将从通常服务不足的社区招募患者和临床医生参与参与设计过程。 这项工作的总体贡献将包括开发和评估：（1）软件技术，联合收割机和转换被动监测和自我报告的数据流到临床上有意义的，可操作的，个性化的指标，我们称之为行为生物标志物;（2）情境回忆，允许收集高度颗粒化和情境特定的自我记忆，报告数据，以从患者的角度增强被动捕获的数据，同时平衡在平衡回忆偏差和可用性方面所面临的紧张局势;以及（3）系统化与临床领域专家合作的方法，以开发和整合行为生物标志物到临床决策中针对特定疾病。我们将创建和评估一个模块化和可扩展的分析和用户交互技术套件，旨在促进迭代实施和评估。这些模块本身就是一种贡献，但同样重要的是评估行为生物标志物作为精准医学驱动因素的整体方法。

The vision of patient-centric, personalized, precision medicine and wellness will be fully realized only when an individual?s self-care and clinical decision making are informed by a rich, predictive model of that individual?s health status. The evolution and dissemination of mobile technology has created unprecedented opportunities for highly detailed and personalized data collection in a far more granular, unobtrusive, and even affordable way; these data include activity levels, location patterns, sleep, consumption, and communication and social interaction. However, turning this potential into practice requires that we develop the algorithms and methodologies to transform these raw data into actionable information. The research will develop novel and generalizable techniques to derive robust measures relevant to individual health and clinical decision making. The team will develop and evaluate tools that convert raw human-activity data into clinically actionable behavioral biomarkers. This demands creative uses of the underlying technical capabilities (i.e., passive data capture, data analysis and machine learning, data visualization, user experience), as well as rigorous understanding of the underlying health condition and management (i.e. functional health measures, achievable and optimal health outcomes, patient challenges in adherence, risks and benefits associated with medication and other aspects of treatment, and clinical decision making). The approach has broad applicability across disease management (e.g., auto-immune, gastrointestinal, depression, cognitive decline, and neurologic disorders), but also calls for tailoring to specific conditions and individuals. Therefore, we will conduct this initial work in a specific context, that of chronic pain management for three prominent conditions: rheumatoid arthritis, osteoarthritis, and lower back pain. The behavioral biomarkers associated with our initial target domain, pain management, center around: (i) decline in activity levels; (ii) increase in stress; (iii) decrease in sleep quality; (iv) drop in function, e.g., reduction in travel distance or inability to go to work. The effectiveness of passive sensing capabilities of the mobile phone to track sleep, changes in activity level, stress, social isolation, geographic location and several other indicators that are likely antecedents or symptoms of pain interference has been demonstrated previously. While behavioral biomarkers rely extensively on passively captured data streams (such as activity, location, communication, application usage and audio), there remain important cases in which self-report data is required to augment or clarify passively collected data. However, the standardized patient survey instruments that assess relevant symptoms and behavior are not suitable for use on a daily basis because of length, question design, or both. Further, traditional forms of self report are often intrusive, burdensome, and suffer high rates of attrition. A new approach, contextual recall, aims to mitigate the issues related to self-report through three key mechanisms: optimizing the delivery of prompts, providing the user with key contextual cues to improve recall, and employing visual input techniques as an alternative to long-form measures that do not scale well to frequent mobile self-reports. The approach to personalizing disease management is intentionally scalable in terms of affordability and accessibility. Passive data collection requires no user attention, and contextual recall is a form of self-report designed for busy individuals with a range of demands and constraints on their time, as well as potential literacy and numeracy constraints. The clinician-facing components of this approach are also designed to work in resource-constrained clinical settings where clinicians are under particular time pressure. The team will recruit patients and clinicians from typically underserved communities to engage in the participatory design process. The overall contributions of this work will include development and evaluation of: (1) software techniques to combine and transform passively monitored and self-reported data streams into clinically meaningful, actionable, and personalized indicators, which we call behavioral biomarkers; (2) contextual recall that allows the collection of highly granular and contextually specific self-report data to enhance passively captured data with information from the patient perspective, while balancing the tension faced in balancing recall bias and usability; and (3) a methodology that systematizes the collaboration with clinical domain experts to develop and integrate behavioral biomarkers into clinical decision making for specific diseases. We will create and evaluate a modular and extensible suite of analytics and user interaction techniques designed to facilitate iterative implementation and evaluation. These modules will themselves be a contribution, but equally important will be the evaluation of the overall approach of behavioral biomarkers as a driver of precision medicine.